My greater team regularly alters client provided data

The statement is rated from 1 to 10 with no good or bad end. The mean rating was 5.4 and the median 5.0 (n = 773 respondents). The rating was associated with all four study measures. Higher ratings came with more cancellation (r = 0.70, 95% CI 0.60 to 0.77, p < 0.001, n = 150 engagements), lower customer satisfaction among active engagements (r = −0.65, 95% CI −0.76 to −0.52, p < 0.001, n = 94 engagements), a lower engagement indicator mean (r = −0.84, 95% CI −0.88 to −0.79, p < 0.001, n = 150 engagements) and higher intent to leave (r = 0.66, cluster bootstrap 95% CI 0.61 to 0.71, p < 0.001 with standard errors clustered by engagement, n = 722 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 52.01, Holm p < 0.001, omega squared 0.484) and respondent role (Welch F 134.69, Holm p < 0.001, omega squared 0.121).

Of the 84 written responses, 83 were negative. The respondents who answered C202 described data that the team cleaned every cycle and that arrived in error again the next, and those who answered C204 described the alteration of the financial institution's data as routine, known to the team lead, seldom logged and seldom reported to the financial institution. The single positive response described a data exception step agreed with the financial institution's platform owner before any correction was applied.

The scale has no good or bad end, so no distressed range applies.

The primary responses numbered 84, written in answer to C204, C202, C210 and C208, the written-answer questions of the indicator's survey section. No response from any other question addressed the indicator, so every response was primary.

In 19 of the 20 responses to C202, respondents said the data arrived dirty or in error again after the team had cleaned it, and most added that they fixed it themselves before it was loaded. Access appeared in 15 responses across all questions: credentials that expired, requests that sat for weeks, or a platform closed to the team, so the data could not be corrected at its source.

The 61 responses to C204 described how often and how openly the team altered the data. Words of frequency, such as every cycle, most weeks or seven out of ten, appeared in 81 responses across all questions. The team lead or manager appeared as someone who knew or was told in 22 responses to C204. In 27 responses, respondents said that nothing changed upstream, that nobody signed off the workaround or that a raised problem went nowhere. Respondents said the fixes were not logged, reported or flagged in 15 responses, and that the financial institution was not told, or that no one outside the team knew, in 9. Three responses said the financial institution had been told in writing or already knew that the team filled gaps in its data.

The themes most often marked were “Occurs every cycle or most days” (74 responses), “Told vendor-side management” (24) and “Raised it, nothing came back” (13).

Of the 84 responses, 83 (98.81%, 95% CI 96.15% to 100.00%) were negative and 82 (97.62%) carried the strongest negative score of −10. Positive responses numbered 1 (1.19%), and no response was neutral. The mean written score was −9.8 (95% CI −10.0 to −9.3, n = 84 responses), and the median was −10.0.

Of the 84 responses, 83 came from respondents who also rated the indicator. Among them, the written score fell with the rating (Spearman rho −0.25, 95% CI −0.40 to −0.15, p = 0.023, n = 83 responses). Respondents who agreed more strongly wrote more negatively.

Respondents who wrote about the indicator and gave a rating numbered 77, with a mean rating of 6.9. The 696 respondents who rated the indicator without writing about it had a mean of 5.2. The difference, 1.7 (95% CI 1.4 to 2.1), excluded zero.

Every figure in the segment tables is at respondent level. Segments with fewer than 30 respondents carry no interval. Segments with fewer than 10 report counts only.

Segment families recorded at engagement level were tested on engagement means. Respondent role and vendor type were tested on respondents. After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 52.01, Holm p < 0.001, omega squared 0.484). Canceled engagements rated highest (mean 6.9, n = 287 respondents) and active engagements in Production lowest (mean 4.2, n = 93 respondents). Ratings also differed by respondent role (Welch F 134.69, Holm p < 0.001, omega squared 0.121). Individual contributors rated highest (mean 5.7, n = 622 respondents) and portfolio directors lowest (mean 2.5, n = 20 respondents). Outsourcing model, institution type, process family, class of work and vendor type showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 55 engagements with a written response (Kruskal-Wallis H 3.20, df 3, p = 0.362). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −10.0, 48 responses) and responses from active engagements in Transition/Cut-Over the least negative (mean −9.1, 17 responses). Responses from active engagements in Production (8) are reported as counts only.

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 73 responses, engagement managers 10 and portfolio directors 1.

Cancellation rate is the share of engagements canceled by the study date, measured at engagement level (150 engagements, 56 canceled). Canceled engagements were rated after cancellation. The association is therefore a descriptive contrast and not a forecast of cancellation.

Across the study set, higher ratings came with more cancellation (r = 0.70, 95% CI 0.60 to 0.77, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in eighteen of the 20 segment levels tested.

Customer satisfaction is the percentage recorded against each engagement. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

Across the active engagements, higher ratings came with lower customer satisfaction (r = −0.65, 95% CI −0.76 to −0.52, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in thirteen of the 16 segment levels tested.

The engagement indicator mean is the mean of an engagement's ratings across the rated indicators, measured at engagement level (150 engagements). The indicator does not enter the mean.

Higher ratings came with a lower engagement indicator mean (r = −0.84, 95% CI −0.88 to −0.79, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twenty-four of the 24 segment levels tested.

Intent to leave is the answer to C019, “How likely are you to leave your current job in the next 12 months?”, rated 1 to 10. It is measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

Respondents who gave higher ratings reported higher intent to leave (r = 0.66, 95% CI 0.62 to 0.70, Holm p < 0.001, n = 722 respondents). The association held after Holm correction in thirty-six of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 95 other engagements, 95 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 55 engagements with a negative response, 27 (49.09%) were canceled, against 29 of the 95 other engagements (30.53%). The difference reached significance (Fisher exact p = 0.035). Customer satisfaction among active engagements differed between the two groups (mean 59.32% on 28 engagements against 66.12% on 66, Welch t −2.71, p = 0.009).

Two correlations test written sentiment directly. Across the 55 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.19, p = 0.172). Across 71 respondents, the respondent mean written score was not associated with intent to leave (Spearman rho −0.07, p = 0.549). Of the four comparisons, two reached significance at 0.05.

The four associations were compared in pairs on their engagement-level forms, each oriented so that a higher value is the better outcome, across the 150 engagements with all four measures. After Holm correction, three of the six pairs differed. The association with the engagement indicator mean was the strongest and the association with customer satisfaction the weakest.

Answers clustered within engagements (ICC 0.54). Clustering reduced the 773 respondent answers to the equivalent of about 239 independent answers. Respondent-level intervals therefore used the cluster bootstrap. Respondent-level tests of intent to leave used standard errors that allow for clustering by engagement.

No association was equivalent to zero. Each of the four differed from zero. The detection floor shown is the study floor at 150 engagements. Intent to leave was tested on its effective sample size.

Each change was computed before rounding and may differ by 0.01 from the difference between the two printed values.

All 22 checks held. A check holds when its test falls on the same side of 0.05 as the primary test. Where the primary test falls below 0.05, the check must also keep the sign of the primary correlation. The thin-base check removes the engagements in any level of outsourcing model or process family with fewer than 30 engagements. Institution type is not used for that check, because every one of its levels has fewer than 30 engagements.

Each of the four measures was computed from data the indicator does not contain. Each result is therefore a finding and not an arithmetic consequence.

Each deviation was computed before rounding and may differ by 0.01 from the difference between the two printed values.

The comparison covered 150 engagements. The indicator departed from the study's typical pattern on all four measures. Its association was weaker than the norm on cancellation, customer satisfaction, the engagement indicator mean and intent to leave.

Among the 13 Part 5 indicators with 30 or more written responses, the indicator's share of negative responses, 98.81%, ranked 5th from the highest, against a median of 98.03%. Its mean written score, −9.8, ranked 1st from the most negative, against a median of −6.3.

The indicator most often present in the same responses was C199, “I have altered client-provided data”, in 74 of the 84 responses (88.10%). C094, “Rate the quality of the customer's data across the systems this engagement depends on.” (45 responses), C200, “I discussed the data alteration with internal management” (26 responses), C201, “I or my management discussed data alteration with the client” (14 responses) and C108, “Rate the customer's ability to provision the system access and identities that AI agents require.” (12 responses) followed.

The most frequent theme was “Occurs every cycle or most days”, in 74 responses (88.10%). The second was “Told vendor-side management”, in 24 responses.

Stronger agreement that the wider team regularly alters the data provided by the financial institution came with more cancellation, lower customer satisfaction among active engagements, a lower engagement indicator mean and higher intent to leave, and every association held under each of the 22 robustness checks. Respondents on canceled engagements rated the statement well above those on the three active stages (mean 6.9, n = 287 respondents).

The written evidence describes the alteration of data as the team's routine answer to source data that arrived in error every cycle and could not be corrected at its source. The team lead usually knew, the fixes were seldom recorded, and the financial institution was seldom told. The associations do not establish that the alterations produced the outcomes. The written responses describe them as one response to the same gaps in data readiness and access that the Part 5 ratings record.

The evidence is the reported experience of the vendor's delivery teams, collected in a single cross-section. The associations reported here are not causal.

Tests by respondent role and by vendor type treated respondents as independent. The section on clustering reports how far clustering within engagements reduced the information in respondent-level figures.

Shares of written sentiment are shares of responses and not of the study set of respondents. The written score takes five fixed values, so its mean and median are coarse. Responses clustered within engagements (84 responses on 55 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.

The written responses are counted by sets of words stated in each rule. A response that described a practice in other words is not counted, so each count is a floor.